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Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven Approaches Supporting Information

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Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven Approaches Supporting Information Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven Approaches Supporting Information Matteo Aldeghi∗[a], Vytautas Gapsys[a] and Bert L. de Groot†[a] [a]Computational Biomolecular Dynamics Group, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany This PDF file includes: • Methods • Figures S1-S11 • Tables S1-S2 Other supporting materials for this manuscript include the following: • Data S1: detailed information on the data set and numerical results for all calculations, provided as an Excel spreadsheet (XLSX). • Data S2: input files pertaining to the molecular-dynamics-based free energy calculations and Rosetta calculations, provided as a compressed archive file (ZIP, 1.6 MB). • Data S3: input files and Jupyter notebooks used for the training and testing of the machine learning model, provided as a compressed archive file (ZIP, 832 MB). This file can be downloaded from the following address: https://doi.org/10.5281/zenodo.3350897. ∗[email protected] †[email protected] S1 Contents Methods S3 Dataset . S3 System Setup . S3 Free Energy Calculations . S4 Rosetta Calculations . S4 Machine Learning . S5 Training Dataset . S5 Features and Feature Selection . S5 Data Analysis . S6 Figures S7 Figure S1 Thermodynamic cycle used in the MD-based calculations . S7 Figure S2 Accuracy of the ∆∆G estimates for all force fields . S8 Figure S3 Effect of halogen bond modelling on Charmm calculations . S9 Figure S4 Analysis of the consensus force field approach . S10 Figure S5 Results by net charge change . S11 Figure S6 Uncertainty of the MD-based calculations . S12 Figure S7 Analysis of the combined Rosetta+MD consensus approach . S13 Figure S8 Scatter plot of ∆∆G estimates obtained with the avg(A99,R15) consensus score S14 Figure S9 Scatter plot of ∆∆G estimates obtained with mCSM-Lig . S15 Figure S10 Precision-recall curves . S16 Figure S11 Scatter plot of ∆∆G estimates obtained with the max(A99,R15) consensus score S17 Tables S18 Table S1 Summary of all MD-based calculations and their performance . S18 Table S2 Binary classification performance . S19 References S20 S2 Methods Dataset A dataset containing 144 binding affinity changes (∆∆G) for eight TKIs due to point mutations in human Abl kinase is provided by Hauser et al.[1]. The authors also provide structure files of the Abl:TKI complexes. Six of these are structures resolved experimentally via X-ray crystallography (4WA9, 3UE4, 4XEY, 1OPJ, 3CS9, 3OXZ) and two were obtained via docking (referred to as DOK1 and DOK2). For calculations using Charmm force fields, glycine mutations were excluded, as it is currently not possible to interpolate between different grid-based energy correction maps (CMAPs) in Gromacs. Thus, the effective dataset size used in Charmm calculations was of 137 ∆∆G values rather than 144. The standard error associated with the ∆∆G values was taken to be 0.32 kcal/mol, based on the interlaboratory variability of the IC50 derived ∆∆G measurements.[1]. Details of the dataset can be found in Data S1. System Setup The structures of the Abl:TKI complexes were taken from Hauser et al.[1]. Apo structures were generated by discarding the ligand atoms. Crystallographic water molecules were retained. All mutant structures were generated using FoldX (v4)[2]. Protein protonation states were assigned at pH 7.4 with the protein preparation tool in HTMD (v1.12)[3], which uses PDB2PQR[4] and PROPKA v3.1[5, 6]. The protonation states of the ligands were kept as they were determined in Hauser et al.[1]. Proteins were modelled with the Amber99sb*-ILDN[7, 8, 9] (we abbreviate this as “A99”), Am- ber14sb[10] (A14), Charmm22*[11, 12, 13] (C22), and Charmm36[14] (C36) force fields. In addition, we also tested a modified version of Amber99sb*-ILDN in which the parameters for hydroxyl groups were adapted as described in Fennel et al.[15]; we refer to this force field as Amber99sb*-ILDN-DC (A99dc). The TIP3P water model[16] was used. Ligands were modelled with GAFF2 (v2.1)[17] via AmberTools 16 and CGenFF (v3.0.1)[18] via paramchem[19, 20]. In the GAFF2 models, restrained electrostatic potential (RESP)[21] charges were used. Geometry optimizations and molecular elec- trostatic potential calculations (ESP) were performed with Gaussian 09 (Rev. D.01), both at the HF/6-31G* level of theory. Only 3 optimization steps were carried out to keep ligands’ conformations close to the bound poses. ESP points were sampled according to the Merz-Kollman scheme[22, 23]. In addition, the σ-hole on halogen atoms was modelled as described by Kolář and Hobza[24]. All ligand parameters can be found in the input files in Data S2. The protein-ligand systems were solvated in a dodecahedral box with periodic boundary conditions and a minimum distance between the solute and the box of 12 Å. Sodium and chloride ions were added to neutralize the wild type systems at the concentration of 0.15 M. For the mutant systems, the same number of ions as in the wild type systems was added; i.e. the net charge of the wild type systems was always zero, while the net charge of the mutant systems was allowed to deviate from zero according to the mutation. Because FoldX does not consider the presence of ligands when mutating the protein, clashes with the ligands in the mutated complexes may occur. A clash was considered present if any protein heavy atom was within 1.5 Å of any ligand heavy atom. If one or more clashes were present, an approach similar to the one reported for alchembed[25] was used to resolve them: after 2,000 steepest descent steps, the ligand vdW interactions were switched on in 2,000 MD steps carried out with a 0.5 fs timestep, while using position restraints (1,000 kJ mol−1 nm−2) on all heavy atoms. S3 Free Energy Calculations All simulations were carried out in Gromacs 2016[26, 27] on cluster nodes equipped with an Intel Xeon processor of Ivy Bridge (4 cores, e.g. E3-1270 v2) or Broadwell (10 cores, e.g. E5-2630 v4) architecture and a Nvidia GeForce GPU 10 series (GTX 1070, GTX 1080, or GTX 1080 Ti). Compute times for a single ∆∆G estimate with a representative node architecture are shown in Table 1. 10,000 energy minimization steps were performed using a steepest descent algorithm. The systems were subsequently simulated for 100 ps in the isothermal-isobaric ensemble (NPT) with harmonic position restraints applied to all solute heavy atoms with a force constant of 1,000 kJ mol−1 nm−2. Equations of motion were integrated with a leap-frog integrator and a time-step of 2 fs. The tempera- ture was coupled with the stochastic v-rescale thermostat of Bussi et al.[28] at the target temperature of 300 K. The pressure was controlled with the Berendsen weak coupling algorithm[29] at a target pressure of 1 bar. The particle mesh Ewald (PME) algorithm[30] was used for electrostatic interac- tions with a real space cut-off of 10 Å when using Amber force fields and 12 Å when using Charmm force fields, a spline order of 4, a relative tolerance of 10−5 and a Fourier spacing of 1.2 Å. The Verlet cut-off scheme[31] with the potential-shift modifier was used with a Lennard-Jones interaction cut-off of 10 Å with Amber and 12 Å with Charmm force fields, and a buffer tolerance of 0.005 kJ mol−1 ps−1. All bonds were constrained with the P-LINCS algorithm[32]. For equilibration, 1 ns unrestrained MD simulations were then performed in the NPT ensemble with the Parrinello-Rahman pressure coupling algorithm[33] at 1 bar with a time constant of 2 ps. Production simulations were then performed for 3 ns. For the more expensive A99` protocol, simulations of 5 ns were used. For each free energy calculation, the above procedure for equilibrium simulations (from system setup to minimization, equilibration, and production MD) was repeated ten times on both the apo and complex states, of both wild-type and mutant Abl kinase, for each ∆∆G estimate. From each of these ten equilibrium simulations, 30 equally spaced frames were extracted as the starting configurations for the non-equilibrium part of the calculations, for a total of 300 non-equilibrium trajectories (in both directions, wild-type to mutant and mutant to wild-type) for each mutation. For A99`, ten repeated equilibrium simulations were used for charge-conserving mutations, and twenty for charge-changing mutations; from these, a total of 400 frames for charge-conserving mutations, and 800 frames for charge-changing mutations, were extracted. The non-interacting (“dummy”) atoms needed to morph the wild-type residues into mutant ones were introduced at this stage with the pmx package[34], using the mutant structure proposed by FoldX as a template. The positions of the dummy atoms were minimized while freezing the rest of the system. These systems containing hybrid residues were then simulated for 10 ps to equilibrate velocities. Amino acid side chains were finally alchemically morphed at constant speed during non-equilibrium simulations of 80 ps in length (100 ps were used for A99`). The work values associated with each non-equilibrium transition were extracted using thermodynamic integration (TI)[35] and then used to estimate the free energy differences with the Bennett’s Acceptance Ratio (BAR)[36, 37, 38]. apo holo Point estimates of the free energy differences (Figure S1: ∆GWT →MT and ∆GWT →MT ) were cal- culated with BAR after pooling all available forward and reverse work values coming from the non- apo equilibrium trajectories spawned from all equilibrium simulation repeats. Uncertainties in ∆GWT →MT holo and ∆GWT →MT were estimated as standard errors (σ∆G) by separately considering each equilibrium simulation and its related non-equilibrium trajectories as independent calculations.
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